

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer



class        A__:
						"""simple docstring"""






						def __init__(    self  ,      _lowercase  ,      _lowercase=13  ,      _lowercase=7  ,      _lowercase=True  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=99  ,      _lowercase=32  ,      _lowercase=5  ,      _lowercase=4  ,      _lowercase=37  ,      _lowercase="gelu"  ,      _lowercase=0.1  ,      _lowercase=0.1  ,      _lowercase=512  ,      _lowercase=16  ,      _lowercase=2  ,      _lowercase=0.0_2  ,      _lowercase=3  ,      _lowercase=4  ,      _lowercase=None  ,      )     -> Optional[Any]:
									a_						:						Optional[Any] 							= parent
									a_						:						Union[str, Any] 							= batch_size
									a_						:						Optional[Any] 							= seq_length
									a_						:						Any 							= is_training
									a_						:						str 							= use_input_mask
									a_						:						Dict 							= use_token_type_ids
									a_						:						Optional[Any] 							= use_labels
									a_						:						Tuple 							= vocab_size
									a_						:						List[Any] 							= hidden_size
									a_						:						List[Any] 							= num_hidden_layers
									a_						:						Dict 							= num_attention_heads
									a_						:						Tuple 							= intermediate_size
									a_						:						str 							= hidden_act
									a_						:						Dict 							= hidden_dropout_prob
									a_						:						List[str] 							= attention_probs_dropout_prob
									a_						:						Union[str, Any] 							= max_position_embeddings
									a_						:						int 							= type_vocab_size
									a_						:						Optional[Any] 							= type_sequence_label_size
									a_						:						Optional[int] 							= initializer_range
									a_						:						List[Any] 							= num_labels
									a_						:						Optional[Any] 							= num_choices
									a_						:						int 							= scope





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Any 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size    )

									a_						:						str 							= None
									if self.use_input_mask:
												a_						:						int 							= random_attention_mask([self.batch_size, self.seq_length]    )

									a_						:						Tuple 							= None
									if self.use_token_type_ids:
												a_						:						Optional[Any] 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.type_vocab_size    )

									a_						:						Union[str, Any] 							= None
									a_						:						List[Any] 							= None
									a_						:						Any 							= None
									if self.use_labels:
												a_						:						List[Any] 							= ids_tensor([self.batch_size]  ,      self.type_sequence_label_size    )
												a_						:						Tuple 							= ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels    )
												a_						:						Dict 							= ids_tensor([self.batch_size]  ,      self.num_choices    )

									a_						:						List[Any] 							= self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels





						def 			UpperCamelCase__					(    self    )     -> Dict:
									return LlamaConfig(
									    vocab_size=self.vocab_size  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      max_position_embeddings=self.max_position_embeddings  ,      type_vocab_size=self.type_vocab_size  ,      is_decoder=_lowercase  ,      initializer_range=self.initializer_range  ,      )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase    )     -> List[str]:
									a_						:						Tuple 							= LlamaModel(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						str 							= model(_lowercase  ,      attention_mask=_lowercase    )
									a_						:						int 							= model(_lowercase    )
									self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> List[Any]:
									a_						:						Dict 							= True
									a_						:						Tuple 							= LlamaModel(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Any 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      )
									a_						:						Optional[Any] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      )
									a_						:						List[str] 							= model(_lowercase  ,      attention_mask=_lowercase    )
									self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> Tuple:
									a_						:						Union[str, Any] 							= LlamaForCausalLM(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						List[str] 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )     -> Tuple:
									a_						:						List[Any] 							= True
									a_						:						Union[str, Any] 							= True
									a_						:						str 							= LlamaForCausalLM(config=_lowercase    )
									model.to(_lowercase    )
									model.eval()

									# first forward pass
									a_						:						Tuple 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      use_cache=_lowercase  ,      )
									a_						:						Tuple 							= outputs.past_key_values

									# create hypothetical multiple next token and extent to next_input_ids
									a_						:						Dict 							= ids_tensor((self.batch_size, 3)  ,      config.vocab_size    )
									a_						:						Optional[int] 							= ids_tensor((self.batch_size, 3)  ,      vocab_size=2    )

									# append to next input_ids and
									a_						:						Optional[int] 							= torch.cat([input_ids, next_tokens]  ,      dim=-1    )
									a_						:						Dict 							= torch.cat([input_mask, next_mask]  ,      dim=-1    )

									a_						:						Union[str, Any] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      output_hidden_states=_lowercase  ,      )["""hidden_states"""][0]
									a_						:						Optional[Any] 							= model(
									    _lowercase  ,      attention_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      past_key_values=_lowercase  ,      output_hidden_states=_lowercase  ,      )["""hidden_states"""][0]

									# select random slice
									a_						:						List[str] 							= ids_tensor((1,)  ,      output_from_past.shape[-1]    ).item()
									a_						:						List[str] 							= output_from_no_past[:, -3:, random_slice_idx].detach()
									a_						:						Any 							= output_from_past[:, :, random_slice_idx].detach()

									self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]    )

									# test that outputs are equal for slice
									self.parent.assertTrue(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-3    )    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[str] 							= self.prepare_config_and_inputs()
									(
									    (
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					(
									    a_
									)							,					
									)						:						str 							= config_and_inputs
									a_						:						Optional[Any] 							= {"""input_ids""": input_ids, """attention_mask""": input_mask}
									return config, inputs_dict







@require_torch
class        A__(a_,  a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				int       					=			(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
						_A    :				int       					=			(LlamaForCausalLM,) if is_torch_available() else ()
						_A    :				Any       					=			(
						    {
						        '''feature-extraction''': LlamaModel,
						        '''text-classification''': LlamaForSequenceClassification,
						        '''text-generation''': LlamaForCausalLM,
						        '''zero-shot''': LlamaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						_A    :				List[Any]       					=			False
						_A    :				Union[str, Any]       					=			False





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						List[str] 							= LlamaModelTester(self    )
									a_						:						int 							= ConfigTester(self  ,      config_class=_lowercase  ,      hidden_size=37    )





						def 			UpperCamelCase__					(    self    )     -> str:
									self.config_tester.run_common_tests()





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						List[Any] 							= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Any 							= self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
												a_						:						int 							= type
												self.model_tester.create_and_check_model(*_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_							,					a_						:						List[Any] 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						List[str] 							= 3
									a_						:						Union[str, Any] 							= input_dict["""input_ids"""]
									a_						:						int 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						List[Any] 							= ids_tensor([self.model_tester.batch_size]  ,      self.model_tester.type_sequence_label_size    )
									a_						:						Tuple 							= LlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Tuple 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_							,					a_						:						Tuple 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						List[Any] 							= 3
									a_						:						Tuple 							= """single_label_classification"""
									a_						:						List[str] 							= input_dict["""input_ids"""]
									a_						:						Union[str, Any] 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						Tuple 							= ids_tensor([self.model_tester.batch_size]  ,      self.model_tester.type_sequence_label_size    )
									a_						:						Tuple 							= LlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Dict 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_							,					a_						:						Union[str, Any] 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						Tuple 							= 3
									a_						:						Optional[Any] 							= """multi_label_classification"""
									a_						:						str 							= input_dict["""input_ids"""]
									a_						:						Any 							= input_ids.ne(1    ).to(_lowercase    )
									a_						:						str 							= ids_tensor(
									    [self.model_tester.batch_size, config.num_labels]  ,      self.model_tester.type_sequence_label_size    ).to(torch.float    )
									a_						:						List[Any] 							= LlamaForSequenceClassification(_lowercase    )
									model.to(_lowercase    )
									model.eval()
									a_						:						Union[str, Any] 							= model(_lowercase  ,      attention_mask=_lowercase  ,      labels=_lowercase    )
									self.assertEqual(result.logits.shape  ,      (self.model_tester.batch_size, self.model_tester.num_labels)    )





						@unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""    )
						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									pass





						@parameterized.expand([("""linear""",), ("""dynamic""",)]    )
						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Optional[Any]:
									a_							,					a_						:						Dict 							= self.model_tester.prepare_config_and_inputs_for_common()
									a_						:						List[Any] 							= ids_tensor([1, 10]  ,      config.vocab_size    )
									a_						:						Optional[int] 							= ids_tensor([1, int(config.max_position_embeddings * 1.5    )]  ,      config.vocab_size    )

									set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
									a_						:						Any 							= LlamaModel(_lowercase    )
									original_model.to(_lowercase    )
									original_model.eval()
									a_						:						List[Any] 							= original_model(_lowercase    ).last_hidden_state
									a_						:						List[Any] 							= original_model(_lowercase    ).last_hidden_state

									set_seed(42    )  # Fixed seed at init time so the two models get the same random weights
									a_						:						Tuple 							= {"""type""": scaling_type, """factor""": 1_0.0}
									a_						:						Optional[int] 							= LlamaModel(_lowercase    )
									scaled_model.to(_lowercase    )
									scaled_model.eval()
									a_						:						int 							= scaled_model(_lowercase    ).last_hidden_state
									a_						:						int 							= scaled_model(_lowercase    ).last_hidden_state

									# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
									# maximum sequence length, so the outputs for the short input should match.
									if scaling_type == "dynamic":
												self.assertTrue(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )
									else:
												self.assertFalse(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )

									# The output should be different for long inputs
									self.assertFalse(torch.allclose(_lowercase  ,      _lowercase  ,      atol=1e-5    )    )







@require_torch
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""    )
						@slow
						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						List[str] 							= [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									a_						:						List[Any] 							= LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""  ,      device_map="""auto"""    )
									a_						:						Tuple 							= model(torch.tensor([input_ids]    )    )
									# Expected mean on dim = -1
									a_						:						Tuple 							= torch.tensor([[-6.6_5_5_0, -4.1_2_2_7, -4.9_8_5_9, -3.2_4_0_6, 0.8_2_6_2, -3.0_0_3_3, 1.2_9_6_4, -3.3_6_9_9]]    )
									torch.testing.assert_close(out.mean(-1    )  ,      _lowercase  ,      atol=1e-2  ,      rtol=1e-2    )
									# slicing logits[0, 0, 0:30]
									# fmt: off
									a_						:						Tuple 							= torch.tensor([-1_2.8_2_8_1, -7.4_4_5_3, -0.4_6_3_9, -8.0_6_2_5, -7.2_5_0_0, -8.0_0_0_0, -6.4_8_8_3, -7.7_6_9_5, -7.8_4_3_8, -7.0_3_1_2, -6.2_1_8_8, -7.1_3_2_8, -1.8_4_9_6, 1.9_9_6_1, -8.6_2_5_0, -6.7_2_2_7, -1_2.8_2_8_1, -6.9_4_9_2, -7.0_7_4_2, -7.7_8_5_2, -7.5_8_2_0, -7.9_0_6_2, -6.9_3_7_5, -7.9_8_0_5, -8.3_4_3_8, -8.1_5_6_2, -8.0_4_6_9, -7.6_2_5_0, -7.7_4_2_2, -7.3_3_9_8,]    )
									# fmt: on
									torch.testing.assert_close(out[0, 0, :30]  ,      _lowercase  ,      atol=1e-5  ,      rtol=1e-5    )





						@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""    )
						@slow
						def 			UpperCamelCase__					(    self    )     -> str:
									a_						:						Optional[Any] 							= [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									a_						:						str 							= LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""  ,      device_map="""auto"""    )
									a_						:						Dict 							= model(torch.tensor(_lowercase    )    )
									# Expected mean on dim = -1
									a_						:						Union[str, Any] 							= torch.tensor([[-2.0_6_2_2, -1.2_7_9_4, -1.1_6_3_8, -0.9_7_8_8, -1.4_6_0_3, -1.0_2_3_8, -1.7_8_9_3, -1.4_4_1_1]]    )
									torch.testing.assert_close(out.mean(-1    )  ,      _lowercase  ,      atol=1e-2  ,      rtol=1e-2    )
									# slicing logits[0, 0, 0:30]
									# fmt: off
									a_						:						List[Any] 							= torch.tensor([-8.1_4_0_6, -8.0_5_4_7, 2.7_4_6_1, -1.2_3_4_4, -0.1_4_4_8, -1.8_2_6_2, -1.0_0_2_0, -1.8_1_5_4, -1.6_8_9_5, -1.8_5_1_6, -2.3_5_7_4, -0.9_2_7_7, 3.7_5_9_8, 6.5_7_4_2, -1.2_9_9_8, -0.1_1_7_7, -8.1_4_0_6, -2.9_6_8_8, -2.9_1_9_9, -3.1_6_9_9, -3.5_2_5_4, -2.3_5_5_5, -2.7_9_8_8, -3.4_1_4_1, -2.8_2_6_2, -4.5_1_9_5, -3.3_3_7_9, -3.3_1_6_4, -2.7_8_3_2, -3.0_2_7_3]    )
									# fmt: on
									torch.testing.assert_close(out[0, 0, :30]  ,      _lowercase  ,      atol=1e-5  ,      rtol=1e-5    )





						@unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""    )
						@slow
						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Tuple 							= [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									a_						:						str 							= LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""  ,      device_map="""auto"""    )
									a_						:						Union[str, Any] 							= model(torch.tensor(_lowercase    )    )
									# Expected mean on dim = -1
									a_						:						Optional[int] 							= torch.tensor([[-0.8_5_6_2, -1.8_5_2_0, -0.7_5_5_1, -0.4_1_6_2, -1.5_1_6_1, -1.2_0_3_8, -2.4_8_2_3, -2.3_2_5_4]]    )
									torch.testing.assert_close(out.mean(-1    )  ,      _lowercase  ,      atol=1e-2  ,      rtol=1e-2    )
									# slicing logits[0, 0, 0:30]
									# fmt: off
									a_						:						Tuple 							= torch.tensor([-2.2_2_2_7, 4.8_8_2_8, 0.9_0_2_3, -0.4_5_7_8, -0.7_8_7_1, -0.1_0_3_3, -0.6_2_2_1, -0.5_7_8_6, -0.7_8_0_3, -1.0_6_7_4, -1.2_9_2_0, -0.1_5_7_0, 0.8_0_0_8, 2.0_7_2_3, -0.9_4_9_7, 0.2_7_7_1, -2.2_2_2_7, -0.7_6_1_2, -1.4_3_4_6, -1.2_0_6_1, -1.6_4_2_6, -0.3_0_0_0, -0.7_1_3_9, -1.1_9_3_4, -1.8_6_9_1, -1.6_9_7_3, -1.5_9_4_7, -1.2_7_0_5, -0.3_5_2_3, -0.5_5_1_3]    )
									# fmt: on
									torch.testing.assert_close(out.mean(-1    )  ,      _lowercase  ,      atol=1e-2  ,      rtol=1e-2    )





						@unittest.skip(
						    """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""    )
						@slow
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									a_						:						Union[str, Any] 							= [1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
									a_						:						Optional[int] 							= LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""  ,      device_map="""auto"""    )
									a_						:						str 							= model(torch.tensor(_lowercase    )    )

									a_						:						Optional[int] 							= torch.tensor(
									    [[-4.2_3_2_7, -3.3_3_6_0, -4.6_6_6_5, -4.7_6_3_1, -1.8_1_8_0, -3.4_1_7_0, -1.4_2_1_1, -3.1_8_1_0]]  ,      dtype=torch.floataa    )
									torch.testing.assert_close(out.mean(-1    )  ,      _lowercase  ,      atol=1e-2  ,      rtol=1e-2    )
									# fmt: off
									a_						:						Any 							= torch.tensor([-9.4_9_2_2, -3.9_5_5_1, 1.7_9_9_8, -5.6_7_5_8, -5.1_0_5_5, -5.8_9_8_4, -4.8_3_2_0, -6.8_0_8_6, -6.5_3_9_1, -5.6_1_7_2, -5.5_8_2_0, -5.5_3_5_2, 1.7_8_8_1, 3.6_2_8_9, -6.5_1_1_7, -3.4_7_8_5, -9.5_0_0_0, -6.0_3_5_2, -6.8_1_2_5, -6.0_1_9_5, -6.6_8_3_6, -5.4_7_2_7, -6.2_8_1_2, -6.0_3_9_1, -7.3_3_9_8, -7.4_2_9_7, -7.4_8_4_4, -6.5_8_2_0, -5.8_7_8_9, -5.5_3_1_2]    )
									# fmt: on
									torch.testing.assert_close(out[0, 0, :30]  ,      _lowercase  ,      atol=1e-5  ,      rtol=1e-5    )





						@unittest.skip("""Model is curently gated"""    )
						@slow
						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						List[Any] 							= """Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
									a_						:						Optional[int] 							= """Simply put, the theory of relativity states that """
									a_						:						int 							= LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""    )
									a_						:						List[Any] 							= tokenizer.encode(_lowercase  ,      return_tensors="""pt"""    )
									a_						:						List[str] 							= LlamaForCausalLM.from_pretrained(
									    """meta-llama/Llama-2-13b-chat-hf"""  ,      device_map="""sequential"""  ,      use_safetensors=_lowercase    )

									# greedy generation outputs
									a_						:						List[str] 							= model.generate(_lowercase  ,      max_new_tokens=64  ,      top_p=_lowercase  ,      temperature=1  ,      do_sample=_lowercase    )
									a_						:						int 							= tokenizer.decode(generated_ids[0]  ,      skip_special_tokens=_lowercase    )
									self.assertEqual(_lowercase  ,      _lowercase    )





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


